https://github.com/simonpierreboucher/llm_openai_notebook

This repository contains Jupyter notebooks to explore and utilize OpenAI's Large Language Models (LLMs) for various applications, including chatbots, retrieval-augmented generation, text generation, prompt engineering, and vector embedding. These notebooks provide a comprehensive toolkit for working with OpenAI models in diverse contexts.
https://github.com/simonpierreboucher/llm_openai_notebook

chatbot openai openai-api prompt-engineering rag retrieval-augmented-generation text-generation

Last synced: 2 months ago
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This repository contains Jupyter notebooks to explore and utilize OpenAI's Large Language Models (LLMs) for various applications, including chatbots, retrieval-augmented generation, text generation, prompt engineering, and vector embedding. These notebooks provide a comprehensive toolkit for working with OpenAI models in diverse contexts.

• Host: GitHub
• URL: https://github.com/simonpierreboucher/llm_openai_notebook
• Owner: simonpierreboucher
• Created: 2024-11-13T21:22:46.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2024-11-14T19:43:27.000Z (over 1 year ago)
• Last Synced: 2025-06-04T12:30:01.973Z (about 1 year ago)
• Topics: chatbot, openai, openai-api, prompt-engineering, rag, retrieval-augmented-generation, text-generation
• Language: Jupyter Notebook
• Homepage:
• Size: 43 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# LLM OpenAI Notebook

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This repository contains Jupyter notebooks to explore and utilize OpenAI's Large Language Models (LLMs) for various applications, including chatbots, retrieval-augmented generation, text generation, prompt engineering, and vector embedding. These notebooks provide a comprehensive toolkit for working with OpenAI models in diverse contexts.

## Repository Structure

- **[OPENAI-CHAT.ipynb](https://github.com/simonpierreboucher/llm_openai_notebook/blob/main/OPENAI-CHAT.ipynb)**: Demonstrates the setup of a chatbot using OpenAI models, focusing on conversational interactions and response generation.

- **[OPENAI-RAG.ipynb](https://github.com/simonpierreboucher/llm_openai_notebook/blob/main/OPENAI-RAG.ipynb)**: Implements Retrieval-Augmented Generation (RAG), combining retrieval of relevant data with OpenAI model responses for context-aware answers.

- **[OPENAI-TEXTGEN.ipynb](https://github.com/simonpierreboucher/llm_openai_notebook/blob/main/OPENAI-TEXTGEN.ipynb)**: Focuses on text generation using OpenAI models, suitable for creative writing, content creation, and informative text outputs.

- **[OPENAI_PROMPTING.ipynb](https://github.com/simonpierreboucher/llm_openai_notebook/blob/main/OPENAI_PROMPTING.ipynb)**: Provides methods and techniques for effective prompt engineering, demonstrating how to optimize prompts to guide model behavior.

- **[OPENAI_REFERENCE_RAG.ipynb](https://github.com/simonpierreboucher/llm_openai_notebook/blob/main/OPENAI_REFERENCE_RAG.ipynb)**: An advanced notebook on Retrieval-Augmented Generation that includes reference material integration for highly accurate responses.

- **[OPENAI_VECTOR_EMB.ipynb](https://github.com/simonpierreboucher/llm_openai_notebook/blob/main/OPENAI_VECTOR_EMB.ipynb)**: Explores vector embeddings with OpenAI models, showcasing how to use embeddings for similarity search, clustering, and other applications in natural language processing.

## Getting Started

### Prerequisites

To run these notebooks, you will need:

- **Python 3.8+**

- **Jupyter Notebook**

- Dependencies listed in `requirements.txt`

### Installation

1. Clone the repository:

   ```bash

   git clone https://github.com/simonpierreboucher/llm_openai_notebook.git

   cd llm_openai_notebook

   ```

2. Install the dependencies:

   ```bash

   pip install -r requirements.txt

   ```

### Running the Notebooks

1. **Start Jupyter Notebook**: Open Jupyter by navigating to the repository folder and running:

   ```bash

   jupyter notebook

   ```

2. **Select a Notebook**: Open any of the notebooks to explore functionalities such as chat, RAG, or text generation.

3. **Follow Instructions**: Each notebook contains instructions and steps for interacting with OpenAI models in the respective application.

## Use Cases

- **Chatbot Development**: With `OPENAI-CHAT.ipynb` and `OPENAI_PROMPTING.ipynb`, you can create and optimize a conversational agent.

- **Information Retrieval**: Use `OPENAI-RAG.ipynb` and `OPENAI_REFERENCE_RAG.ipynb` for applications that require accurate, source-grounded responses.

- **Content Creation**: `OPENAI-TEXTGEN.ipynb` provides tools for generating creative or informational content.

- **Embedding and Similarity Search**: `OPENAI_VECTOR_EMB.ipynb` is ideal for NLP tasks involving similarity matching, clustering, and more.

## Contributing

We welcome contributions! Feel free to submit issues or pull requests to enhance the functionality, add features, or fix bugs.

## License

This repository is licensed under the MIT License.